NA-MIC National Alliance for Medical Image Computing fMRI within NAMIC Sandy Wells, Polina Golland Discussion moderator: Andy Saykin

National Alliance for Medical Image Computing fMRI Update Algorithms for time-series analysis –Regularization/smoothing –Segmentation/clustering Enabling methodologies –Joint analysis with other modalitites – Group analysis Core 1 / Core 3 projects to apply to clinical data Core 1 / Core 2 projects to integrate into NAMIC-kit

National Alliance for Medical Image Computing fMRI Detection/Regularization Smarter strategies for smoothing –MRF priors (MIT/BWH) Wanmei Ou, Polina Golland, Sandy Wells –Surface-based vs. volumetric smoothing (MGH) Anastasia Yendiki, Doug Greve, Bruce Fischl Example: MIND fMRI reliability study –Sensorimotor paradigm –10 subjects on 2 visits at each of 4 sites –We thank Randy Gollub for providing the MIND data

National Alliance for Medical Image Computing Surface vs. Volume Smoothing Surface Volume Four subjects (fixed-effects, single visit), 15mm FWHM: Demonstrated better detection power

National Alliance for Medical Image Computing Functional Hierarchy/Segmentation Hierarchical clustering of time series data (MIT) –Polina Golland, Bryce Kim, Danial Lashkari, Simultaneously estimate –Representative “signatures” –Which signature best describes each voxel Example: diverse set of visual and mental tasks –localizer, rest, movie, etc.; ~1 hour of fMRI data –7 subjects

National Alliance for Medical Image Computing Hierarchy in Single Subject AuditoryMotor High Visual ? STS+ ? ? VisualMotor+Aud Retino topic High Visual IntrinsicStimulus Dependent STS ?

National Alliance for Medical Image Computing Group Analysis of 2 systems Individual MapsGroup Average

NA-MIC National Alliance for Medical Image Computing Enabling Methodologies Core 1 / Core 2 / Core 3

National Alliance for Medical Image Computing fMRI/DTI Connectivity DTI-based Connectivity Analysis –Path of interest analysis (MGH) –Probabilistic tractography (MT/BWH/Harvard) Strength of connection between ROIs Tri Ngo, C-F Westin, Marek Kubicki, Polina Golland ROIs from fMRI –Color Stroop in Schizophrenia –15 subjects in each group Implementation in NAMIC-kit in progress

National Alliance for Medical Image Computing Anatomical Analysis Cortical segmentation and flattening (MGH) –Freesurfer tools, now compatible with Slicer –Doug Greeve, Bruce Fischl, Steve Pieper Conformal mapping of the cortex (Georgia Tech) –Yi Gao, John Melonakos, Allen Tannebaum –Filters in ITK

National Alliance for Medical Image Computing Population Registration Information-theoretic group-wise alignment (MIT/MGH/BWH) –Integration into NAMIC-kit in progress –In the fututre: non-rigid deformations using B-splines Unaligned input Aligned output –Serdar Balci, Lilla Zollei, Mert Sabuncu, Sandy Wells, Polina Golland

National Alliance for Medical Image Computing EPI Registration/De-Warping Combine segmentation and registration with Physics- based modeling of susceptibility (MIT/BWH/fMRIB) –Accurate registration of fMRI to anatomical MR –Retrospective correction of EPI distortions –Clare Poynton, Sandy Wells, Mark Jenkinson Acquired Estimated